EEG-based cognitive load assessment of eHMIs in pedestrian-automated vehicle interactions: A real-world experiment

External human-machine interfaces (eHMIs) are an emerging technology designed to facilitate communication between road users and highly automated vehicles (HAVs). This study conducted a real-world experiment to evaluate and quantify the cognitive load of pedestrians crossing in front of an eHMI-equipped HAV, utilizing electroencephalography (EEG) data, an objective measure that addresses limitations of prior self-report methods.

A real-world experiment with 24 participants employed a 4x2 repeated-measures design. The independent variables were four eHMI types (no eHMI, light-band, text-based and symbol-based) and two vehicle kinematic conditions (yielding vs. non-yielding). Participants performed road-crossing tasks in front of a HAV. EEG data were recorded and analyzed using microstate analysis. A cognitive load quantification model was developed by integrating multiple microstate temporal parameters (average duration, occurrence frequency, coverage) via the entropy weight method.

In the simple one-on-one interaction scenario, the symbol-based eHMI consistently showed the lowest cognitive load values. Nevertheless, cognitive load did not differ significantly across eHMI types in either the yielding or non-yielding condition. Overall, pedestrians experienced lower cognitive load in non-yielding conditions than in yielding conditions.

In low-complexity interaction scenarios, intuitive eHMI designs impose only minimal additional cognitive demand, while vehicle kinematics remain the primary determinant of pedestrians' cognitive load. The proposed EEG-based quantitative framework offers methodological support for the evaluation and optimization of more complex centralized eHMI designs that convey the behavioral intentions of multiple surrounding HAVs.